**Title:** Supplementary Materials for: "INTRODUCTION TO THE ANALYTICAL METHOD OF EDUCATION CONTROL (AMEC): FROM MATHEMATICAL ANALYSIS OF LEARNING PROCESSES TO A STANDARDIZED EDUCATIONAL DATABASE"

**Author:** Kravtsov, Gennadiy G.
**ORCID:** https://orcid.org/0009-0000-3405-1461
**Related Publication (Russian version):** https://doi.org/10.5281/zenodo.16791743
**License:** Creative Commons Attribution 4.0 International (CC BY 4.0)

### OVERVIEW

This dataset contains supplementary materials for the article "INTRODUCTION TO THE ANALYTICAL METHOD OF EDUCATION CONTROL (AMEC)...". It provides the raw data, statistical analysis, and AI-generated reviews that underpin the research findings and conclusions presented in the main publication. The materials are intended to ensure full transparency, reproducibility, and verifiability of the study.

### FILE DESCRIPTIONS

1.  **`Source_Data_and_Statistical_Analysis_Historical_N1651_for_AMEC_Article_EN.xlsx`**
    *   **Content:** This Excel file contains the source data and comprehensive statistical analysis for the **historical (traditional) educational metrics** (N=1651 cases of teaching practice).
    *   **Includes:**
        *   Raw data table.
        *   Statistical calculations (correlations, descriptive statistics).
        *   Graphs and plots that duplicate and extend those presented in the main article (Figures 5, 6, 7, 11), demonstrating the relationships between traditional metrics like homework load, grade inflation ("strictness"), and intensity of lessons against average grades.
        *   Analysis of distribution patterns (e.g., for history grades in 10th grade).
        *   Analysis of multicollinearity.
    *   **Purpose:** To provide empirical evidence of the limitations and paradoxical correlations inherent in traditional, subjective educational assessment methods, as discussed in the article.

2.  **`Source_Data_and_Statistical_Analysis_N355_for_AMEC_Article_EN.xlsx`**
    *   **Content:** This Excel file contains the source data and statistical analysis for the core **AMEC methodology** (N=355 cases of teaching practice, filtered for "average statistical" classes).
    *   **Includes:**
        *   Raw data table normalized and expressed in the absolute 20-point AMEC scale.
        *   Statistical calculations validating the AMEC approach.
        *   Key graphs demonstrating the functional dependencies discovered using AMEC (Figures 8, 9, 10), such as the strong positive correlation (r=0.824) between homework load and knowledge level in the absolute scale.
        *   Formulas for converting traditional grades to the absolute scale (polynomial regression).
    *   **Purpose:** To provide the data evidence for the proposed Analytical Method of Education Control (AMEC), showcasing its ability to reveal clear, expected, and mathematically formalized relationships within the educational process after controlling for confounding variables.

3.  **`AI_Review_AMEC_Monograph_EN.pdf`**
    *   **Content:** An AI-generated review of the main monograph (article) using the universal prompt for AI-based peer review, as described in the related publication on AI-assisted review (Kravtsov, 2025 - DOI: 10.5281/zenodo.XXXXXXX).
    *   **Purpose:** To demonstrate the practical application of the hybrid peer-review model promoted by the author. This review serves as an example of an objective, algorithmic assessment of the scientific text, highlighting its strengths and potential weaknesses. It is provided for transparency and as a model for open expertise.

### DATA PROCESSING AND METHODOLOGY

The data in the Excel files were processed and analyzed according to the AMEC methodology outlined in the main article:
*   Data normalization and conversion to the absolute 20-point scale.
*   Statistical analysis was performed using standard methods (Pearson correlation, polynomial regression) in Microsoft Excel.
*   The sample N=355 was filtered from the larger dataset (N=1651) to include only classes and teachers closest to the "average statistical" definition, allowing for the isolation of key dependencies without noise from extreme values or uneven groups.

### INSTRUCTION FOR USE

Researchers are encouraged to use these files to:
1.  Verify the calculations and conclusions presented in the article.
2.  Conduct their own secondary analysis on the provided data.
3.  Understand the practical implementation of the AMEC methodology.
4.  Use the AI review as a template for their own self-assessment or research into AI-assisted peer review.

### RELATION TO OTHER WORK

This dataset is part of a larger research project. The Russian-language version of these supplementary materials is available in the updated record of the main publication on Zenodo: https://doi.org/10.5281/zenodo.16791743

### CONTACT INFORMATION

For any questions regarding this dataset, please contact: Gennadiy G. Kravtsov, Director of the Research Center "Applied Statistics". E-mail: 62abc@mail.ru
